Learn about the first performance evaluation of the Intel® Stratix® 10 NX FPGA, optimized for AI. This research compares the Intel Stratix FPGA to current AI-optimized GPUs, NVIDIA* T4 and V100, on a large suite of real-time, deep learning inference workloads.
Read about a proof-of-concept Python*-to-FPGA compiler that is based on the Numba* Just-In-Time (JIT) compiler for Python and the Intel® FPGA SDK for OpenCL™ software technology. It allows for a seamless use of an FPGA card as an accelerator for Python.
This technique uses the Horner scheme to evaluate polynomials and removes the majority of alignment shifters present in floating-point adders by building a fused evaluation operator. The result is a reduction in circuit latency and logic consumption.